In-class Exercise 4

This in-class exercise is a short recap of what is covered in Hands-on Exercise 4, with some concepts being reiterated again.

Installing and Loading Packages

• sf: handle vector data
• spatstat: used for spatial analysis
• raster: handle raster-based geospatial data
• map tools: convert spatial object class to ppp(Spatstat) format
• tmap: handle map plotting
• tidyverse: handle aspatial data

packages = c('sf', 'spatstat', 'raster', 'maptools', 'tmap', 'tidyverse')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Importing Geospatial Data

sg_sf <- st_read(dsn="data/shapefile",
                 layer="CostalOutline")

Reading layer `CostalOutline' from data source 
  `C:\Users\user\Desktop\SMU\Y4S1\IS415\geniceee\IS415_blog\_posts\2021-09-06-in-class-exercise-4\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 60 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2663.926 ymin: 16357.98 xmax: 56047.79 ymax: 50244.03
Projected CRS: SVY21

mpsz_sf <- st_read(dsn="data/shapefile",
                   layer="MP14_SUBZONE_WEB_PL")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\Users\user\Desktop\SMU\Y4S1\IS415\geniceee\IS415_blog\_posts\2021-09-06-in-class-exercise-4\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

Importing Aspatial Data

childcare <- read_rds("data/rds/childcare.rds")
CHAS <- read_rds("data/rds/CHAS.rds")

Converting from aspatial to geospatial

CHAS_sf <- st_as_sf(CHAS, 
                    coords = c("X_COORDINATE", "Y_COORDINATE"),
                    crs=3414)

Note: For CHAS dataset, there are columns which are X and Y coordinates. This means that the dataset is in SVY21, and we only need to assign EPSG code of 3414

childcare$Lat <- as.numeric(childcare$Lat)
childcare$Lng <- as.numeric(childcare$Lng)
childcare_sf <- st_as_sf(childcare,
                         coords = c("Lng", "Lat"),
                         crs=4326) %>%
  st_transform(crs=3414)

Note: Childcare dataset lat and long columns are in decimal degrees. This means that the dataset is in WGS84, so we will need to assign EPSG code of 4326 before transforming the decimal degrees to metres (SVY21) using st_transform

Geospatial Data Wrangling

Converting from sf to Spatial* classes

childcare <- as_Spatial(childcare_sf)
CHAS <- as_Spatial(CHAS_sf)
mpsz <- as_Spatial(mpsz_sf)
sg <- as_Spatial(sg_sf)

Converting from Spatial* classes to sp format

childcare_sp <- as(childcare,"SpatialPoints")
CHAS_sp <- as(CHAS,"SpatialPoints")
sg_sp <- as(sg,"SpatialPolygons")

Key difference between Spatial* classes and sp format: - Spatial* classes still have associated data - sp format will drop data table, will only leave geometries

Converting from sp to spatstat ppp format

childcare_ppp <- as(childcare_sp, "ppp")
CHAS_ppp <- as(CHAS_sp, "ppp")

• ppp format drops the projection, that’s why any spatstat generated output don’t have projection –> when convert back to raster layer, need to put back the georeferencing system

Plotting the points using tmap

tmap_mode('view')
tm_shape(childcare_sf) + 
  tm_dots(alpha=0.4, col="blue", size=0.05) +
tm_shape(CHAS_sf) + 
  tm_dots(alpha=0.4, col="red", size=0.05)

tmap_mode('plot')